DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems
This addresses the critical task of fault localization in power systems to prevent permanent failures, representing an incremental improvement by applying existing computer vision techniques to a domain-specific problem.
The paper tackles the problem of locating temporary faults in power systems by proposing DeVLearn, a deep learning framework that converts PMU time series data into images for training a Variational Auto-Encoder, resulting in well-separated clusters for faults at different locations in a 68-bus network.
Frequently recurring transient faults in a transmission network may be indicative of impending permanent failures. Hence, determining their location is a critical task. This paper proposes a novel image embedding aided deep learning framework called DeVLearn for faulted line location using PMU measurements at generator buses. Inspired by breakthroughs in computer vision, DeVLearn represents measurements (one-dimensional time series data) as two-dimensional unthresholded Recurrent Plot (RP) images. These RP images preserve the temporal relationships present in the original time series and are used to train a deep Variational Auto-Encoder (VAE). The VAE learns the distribution of latent features in the images. Our results show that for faults on two different lines in the IEEE 68-bus network, DeVLearn is able to project PMU measurements into a two-dimensional space such that data for faults at different locations separate into well-defined clusters. This compressed representation may then be used with off-the-shelf classifiers for determining fault location. The efficacy of the proposed framework is demonstrated using local voltage magnitude measurements at two generator buses.